import pandas as pd
import requests
from backend.address2UPRN.main import (
    resolve_uprns_for_postcode_group,
    get_epc_data_with_postcode,
)
from tqdm import tqdm


def sanitise_postcode(postcode: str) -> str | None:
    """
    Normalise postcode for grouping.

    - Uppercase
    - Remove all whitespace
    """
    if pd.isna(postcode):
        return None

    return postcode.upper().replace(" ", "")


def is_valid_postcode(postcode_clean: str) -> bool:
    """
    Validate postcode using postcodes.io.

    Expects a sanitised postcode (e.g. E84SQ).
    Returns True if valid, False otherwise.
    """
    POSTCODES_IO_VALIDATE_URL = "https://api.postcodes.io/postcodes/{postcode}/validate"
    if not postcode_clean:
        return False

    try:
        resp = requests.get(
            POSTCODES_IO_VALIDATE_URL.format(postcode=postcode_clean),
            timeout=5,
        )
        resp.raise_for_status()
        return resp.json().get("result", False)
    except requests.RequestException:
        # Network issues, rate limits, etc.
        return False


def main():
    df = pd.read_excel("hackney.xlsx", sheet_name="Sustainability")
    df = df.head(500)

    # Sanitise postcodes
    df["postcode_clean"] = df["Postcode"].apply(sanitise_postcode)

    # --- validate AFTER grouping (save API calls) ---

    # Get unique, non-null postcodes
    unique_postcodes = df["postcode_clean"].dropna().unique()

    # Validate each postcode once, TODOadd a progress bar
    postcode_validity = {
        pc: is_valid_postcode(pc)
        for pc in tqdm(unique_postcodes, total=len(unique_postcodes))
    }

    # Map validity back onto dataframe
    df["postcode_valid"] = df["postcode_clean"].map(postcode_validity)

    results = []

    for postcode, group_df in tqdm(
        df[df["postcode_valid"]].groupby("postcode_clean"),
        desc="Resolving UPRNs by postcode",
    ):
        try:
            epc_df = get_epc_data_with_postcode(postcode)

            if epc_df.empty:
                tmp = group_df.copy()
                tmp["found_uprn"] = None
                tmp["status"] = "no_epc_results"
                results.append(tmp)
                continue

            resolved = resolve_uprns_for_postcode_group(
                group_df=group_df,
                epc_df=epc_df,
            )

            results.append(resolved)

        except Exception as e:
            tmp = group_df.copy()
            tmp["found_uprn"] = None
            tmp["status"] = "exception"
            tmp["error"] = str(e)
            results.append(tmp)

    final_df = pd.concat(results, ignore_index=True)
    a = final_df[
        [
            "best_match_lexiscore",
            "Address 1",
            "best_match_address",
            "Postcode",
            "UPRN",
            "best_match_uprn",
        ]
    ]  # add levi score to viewing
    b = final_df[final_df["best_match_lexiscore"] > 0]  # add levi score to viewing
    b = b[
        [
            "best_match_lexiscore",
            "Address 1",
            "best_match_address",
            "Postcode",
            "UPRN",
            "best_match_uprn",
        ]
    ]


def handler(event, context):
    print("hello Postcode splitter world")
    return {"statusCode": 200, "body": "hello world"}


if __name__ == "__main__":
    main()